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on
Zero
Running
on
Zero
import os | |
import argparse | |
def parse_args(): | |
parser = argparse.ArgumentParser(description="Simple example of a training script.") | |
parser.add_argument( | |
"--pretrained_model_name_or_path", | |
type=str, | |
default=None, | |
required=True, | |
help="Path to pretrained model or model identifier from huggingface.co/models.", | |
) | |
parser.add_argument( | |
"--variant", | |
type=str, | |
default=None, | |
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16", | |
) | |
parser.add_argument( | |
"--num_frames", | |
type=int, | |
default=25, | |
help="Number of frames that should be generated in the video.", | |
) | |
parser.add_argument( | |
"--train_data_dir", | |
type=str, | |
default=None, | |
required=True, | |
help=( | |
"A folder containing the training data. Folder contents must follow the structure described in" | |
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file" | |
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified." | |
), | |
) | |
parser.add_argument( | |
"--max_train_samples", | |
type=int, | |
default=None, | |
help=( | |
"For debugging purposes or quicker training, truncate the number of training examples to this " | |
"value if set." | |
), | |
) | |
parser.add_argument( | |
"--double_sampling_rate", | |
action="store_true", | |
help=( | |
"whether or not sampling training frames double rate" | |
), | |
) | |
parser.add_argument( | |
"--validation_data_dir", type=str, default=None, help="A prompt that is sampled during training for inference." | |
) | |
parser.add_argument( | |
"--num_validation_images", | |
type=int, | |
default=4, | |
help="Number of images that should be generated during validation with `validation_prompt`.", | |
) | |
parser.add_argument( | |
"--validation_epochs", | |
type=int, | |
default=1, | |
help=( | |
"Run fine-tuning validation every X epochs. The validation process consists of running the prompt" | |
" `args.validation_prompt` multiple times: `args.num_validation_images`." | |
), | |
) | |
parser.add_argument( | |
"--output_dir", | |
type=str, | |
default="sd-model-finetuned-lora", | |
help="The output directory where the model predictions and checkpoints will be written.", | |
) | |
parser.add_argument( | |
"--cache_dir", | |
type=str, | |
default=None, | |
help="The directory where the downloaded models and datasets will be stored.", | |
) | |
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") | |
parser.add_argument( | |
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader." | |
) | |
parser.add_argument("--num_train_epochs", type=int, default=100) | |
parser.add_argument( | |
"--max_train_steps", | |
type=int, | |
default=None, | |
help="Total number of training steps to perform. If provided, overrides num_train_epochs.", | |
) | |
parser.add_argument( | |
"--gradient_accumulation_steps", | |
type=int, | |
default=1, | |
help="Number of updates steps to accumulate before performing a backward/update pass.", | |
) | |
parser.add_argument( | |
"--gradient_checkpointing", | |
action="store_true", | |
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", | |
) | |
parser.add_argument( | |
"--learning_rate", | |
type=float, | |
default=1e-4, | |
help="Initial learning rate (after the potential warmup period) to use.", | |
) | |
parser.add_argument( | |
"--scale_lr", | |
action="store_true", | |
default=False, | |
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", | |
) | |
parser.add_argument( | |
"--lr_scheduler", | |
type=str, | |
default="constant", | |
help=( | |
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' | |
' "constant", "constant_with_warmup"]' | |
), | |
) | |
parser.add_argument( | |
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." | |
) | |
parser.add_argument( | |
"--allow_tf32", | |
action="store_true", | |
help=( | |
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" | |
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" | |
), | |
) | |
parser.add_argument( | |
"--dataloader_num_workers", | |
type=int, | |
default=0, | |
help=( | |
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." | |
), | |
) | |
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") | |
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") | |
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") | |
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") | |
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") | |
parser.add_argument( | |
"--prediction_type", | |
type=str, | |
default=None, | |
help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.", | |
) | |
parser.add_argument( | |
"--logging_dir", | |
type=str, | |
default="logs", | |
help=( | |
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" | |
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." | |
), | |
) | |
parser.add_argument( | |
"--mixed_precision", | |
type=str, | |
default=None, | |
choices=["no", "fp16", "bf16"], | |
help=( | |
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" | |
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" | |
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." | |
), | |
) | |
parser.add_argument( | |
"--report_to", | |
type=str, | |
default="tensorboard", | |
help=( | |
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' | |
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' | |
), | |
) | |
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") | |
parser.add_argument( | |
"--checkpointing_steps", | |
type=int, | |
default=500, | |
help=( | |
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming" | |
" training using `--resume_from_checkpoint`." | |
), | |
) | |
parser.add_argument( | |
"--checkpoints_total_limit", | |
type=int, | |
default=None, | |
help=("Max number of checkpoints to store."), | |
) | |
parser.add_argument( | |
"--resume_from_checkpoint", | |
type=str, | |
default=None, | |
help=( | |
"Whether training should be resumed from a previous checkpoint. Use a path saved by" | |
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' | |
), | |
) | |
parser.add_argument( | |
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers." | |
) | |
parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.") | |
parser.add_argument( | |
"--rank", | |
type=int, | |
default=4, | |
help=("The dimension of the LoRA update matrices."), | |
) | |
args = parser.parse_args() | |
env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) | |
if env_local_rank != -1 and env_local_rank != args.local_rank: | |
args.local_rank = env_local_rank | |
return args | |